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Data Science: Machine Learning

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Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors. In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, and how to use a set of data to discover potentially predictive relationships.


Practical Deep Learning: Real World Deep Learning Projects.

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In short, machine learning algorithms are able to detect and learn from patterns in data and make their own predictions. In traditional programming, someone writes a series of instructions so that a computer can transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action. Machine learning, on the other hand, is an automated process that enables machines to solve problems and take actions based on past observations. Basically, the machine learning process includes these stages: Feed a machine learning algorithm examples of input data and a series of expected tags for that input.


Data Science A-Z : Real-Life Data Science Exercises Included

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Free Coupon Discount - Data Science A-Z: Real-Life Data Science Exercises Included, Learn Data Science step by step through real Analytics examples. Created by Kirill Eremenko, SuperDataScience Team Students also bought Deep Learning A-Z: Hands-On Artificial Neural Networks Machine Learning A-Z: Hands-On Python & R In Data Science Careers in Data Science A-Z Talend Data Integration course Basics,Advanced & ADMIN Python A-Z: Python For Data Science With Real Exercises! Preview this Udemy Course GET COUPON CODE Description Extremely Hands-On... Incredibly Practical... Unbelievably Real! This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.


IBM Research Engineer Abhishek Bhandwaldar On His Machine Learning Journey

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Analytics India Magazine got in touch with Abhishek Bhandwaldar, Research Engineer at IBM to understand his machine learning journey. Abhishek has a Master's in Computer Science from the University of North Carolina. "It is important to have a basic understanding of the different topics in the field to make sure you end up in the area you feel most passionate about," says Abhishek. Abhishek: My introduction to AI was through video games. Then, I read about how'Deep Blue' devised long-term strategies and beat an expert opponent in chess.


Getting Smart through Machine Learning. How Does Machine Learn?

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Most of you would have shopped on Amazon. Now when you go into Amazon you see that there are products recommended to you. Who do u think that could have happened. So this is something known as a recommendation engine and a recommendation engine is nothing but a component of machine learning. So let say you and your friend buy similar products to a friend buys five products and you buy three product.


Guest Editorial: Active Learning for Optimal Experiment Design in High Energy Physics

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This entry is a part of the NYU Center for Data Science blog's recurring guest editorial series. Irina Espejo Morales is a CDS Ph.D. student in data science and also a DeepMind fellow. Kyle Cranmer is a CDS professor of data science and professor of physics at the NYU College of Arts & Science. Lukas Heinrich is a staff scientist at CERN working with the ATLAS experiment at the LHC and former NYU graduate student. Gilles Louppe is an associate professor in artificial intelligence and deep learning at the University of Liège (Belgium) and former Moore Sloan fellow.


Applied Machine Learning in Python

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This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.


How an AI learning curriculum can prepare learners for the future?

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According to estimates, the AI market will contribute USD 15.3 trillion to the global economy by 2030. Considering this, it makes sense for schools to implement an AI learning curriculum that will train the students right from a young age.


130+ Exercises - Python Programming - Data Science - Pandas

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Some topics you will find in the exercises: working with DatetimeIndex working with DataFrames reading/writing files working with different data types in DataFrames working with indexes working with missing values computing correlation concatenating DataFrames calculating cumulative statistics working with duplicate values preparing data to machine learning models working with csv and json filles The course is designed for people who have basic knowledge in Python, NumPy and Pandas. It consists of 130 exercises with solutions. This is a great test for people who are learning the Python language and data science and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course. If you're wondering if it's worth taking a step towards Python, don't hesitate any longer and take the challenge today.


A Guide to Selecting Machine Learning Models in Python

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Model testing is a key part of model building. When done correctly, testing ensures your model is stable and isn't overfit. The three most well-known methods of model testing are randomized train-test split, K-fold cross-validation, and leave one out cross-validation. Feature selection is another important part of model building as it directly impacts model performance and interpretability. The simplest method of feature selection is manual, which is ideally guided by domain expertise.